the basics of network computing

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MICHAEL T. HEANEY UNIVERSITY OF MICHIGAN AUGUST 31, 2011 3-HOUR LESSON The Basics of Network Computing

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The Basics of Network Computing. Michael T. Heaney University of Michigan August 31, 2011 3-Hour lesson. Plan for the Afternoon. Choosing a Network Program Working with Network Data Basic network statistics Visualization. Principal Tasks of Network Computing. Visualization of Networks - PowerPoint PPT Presentation

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Page 1: The Basics of Network Computing

MICHAEL T. HEANEY

UNIVERSITY OF MICHIGANAUGUST 31 , 20113-HOUR LESSON

The Basics of Network Computing

Page 2: The Basics of Network Computing

Plan for the Afternoon

Choosing a Network ProgramWorking with Network DataBasic network statisticsVisualization

Page 3: The Basics of Network Computing

Principal Tasks of Network Computing

Visualization of Networks

Calculation of Descriptive Statistics

Advanced Network Analysis (e.g., ERGM)

When considering which statistical package to use, consider which of the above tasks your work will focus on.

Page 4: The Basics of Network Computing

UCINet

Operates well in the familiar windows environment, but may be difficult to use with Apple computers.

Allows calculation of most standard network statistics, but is less adept at handling advanced analysis (e.g., ERGM).

Point-and-click approach is relatively easy to learn, but it can be a bit clunky.

Available here: http://www.analytictech.com/ucinet/download.htm

Page 5: The Basics of Network Computing

Statnet in R

Operates well in both Windows and Apple computing environments

Performs both basic and advanced network analyses

Users can develop own network analysis routines

Steep learning curve

Available here: http://statnetproject.org/

Page 6: The Basics of Network Computing

Some Other Packages

MelNet – Specializes in Exponential Random Graph Models. Available: http://www.sna.unimelb.edu.au/

Pajek – Specializes in large network analysis. Available: http://vlado.fmf.uni-lj.si/pub/networks/pajek/

SoNIA – Visualizing Dynamic Networks. Available: http://www.stanford.edu/group/sonia/

And more…..

Page 7: The Basics of Network Computing

UCINet

A good place to start training even if you are going to shift to another program.

Page 8: The Basics of Network Computing

Importing Data

Simplest approach is to read an Excel file.

1. Open UCINet2. Click on Spreadsheet Icon3. File Open Excel Files Filename.xlsx4. In this case, open Hrmatrix.xlsx5. Save as UCINET 7 dataset6. Note the creation of two files filename.##h

and filename. ##d – you will need both of these files in order to use UCINET data.

Page 9: The Basics of Network Computing

Data List Files

A good alternative when you are working with large data sets

Create using a simple text file:

dl nr = 1945 nc = 525, format = edgelist2,labels embeddeddata:10270716051 Communist10270716049 UFPJ10270716048 BrooklynPeace10270716045 BrooklynPeace10270716045 UFPJ

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Page 11: The Basics of Network Computing

Read a Data List File

Data Import Text File DL… Contact_Network_Data OK

Page 12: The Basics of Network Computing

More Varied DL Formats for Data

Best to learn this on your own using UCINet help

Help Help Topics DL

Page 13: The Basics of Network Computing
Page 14: The Basics of Network Computing

Basic Data Analysis – Density

Network Cohesion (new) Density Overall Hrmatrix

Page 15: The Basics of Network Computing
Page 16: The Basics of Network Computing

Compute Density with Two-Mode Data

Network 2-Mode networks 2-mode Cohesion Input 2-mode incidence matrix OK

Page 17: The Basics of Network Computing
Page 18: The Basics of Network Computing

Basic Network Analysis – Centrality

Network Centrality and Power Multiple Measures (old)

Page 19: The Basics of Network Computing
Page 20: The Basics of Network Computing

Using Your Centrality Data in Statistical Analysis

Spreadsheet File Open CentralitySave as type ExcelExcel File Open

Page 21: The Basics of Network Computing

Compute Centrality with Two-Mode Data

Network 2-Mode Networks 2-Mode Centrality Input 2-mode matrix Contact_Network_Data.##h OK

Page 22: The Basics of Network Computing
Page 23: The Basics of Network Computing

Convert Two-Mode Data to One-Mode Data

Data Affiliations (2-mode to 1-mode) Input data … Contact_Network_Data Which mode Column [for this particular example]

Page 24: The Basics of Network Computing
Page 25: The Basics of Network Computing

Using Your Affiliation Data

Note that your new one-mode data (i.e., affiliation data) has been saved as a new file: Contact_Network_Data-ColAff

You can conduct all network analysis on this dataset

Let’s look at it:Spreadsheet File Open

Contact_Network_Data-ColAff OKNote that your cells make are counts of

affiliations, which is why we call this affiliation data

Page 26: The Basics of Network Computing
Page 27: The Basics of Network Computing

Dichotomizing Data

Are data may be valued, but we may preferred that they be dichotomous

Transform Dichotomize Contact_Network_Data-ColAff

Our output will now have only 1s and 0s

Page 28: The Basics of Network Computing
Page 29: The Basics of Network Computing

Basic Visualization

Visualize NetdrawFile Open Ucinet Dataset Network

Choose File

Page 30: The Basics of Network Computing

Refine Visualization

Open Ucinet dataset Attribute data HRattributes

Properties Lines Arrow Heads Visible Off

Properties Nodes Symbols Size Attribute Based Age

Properties Nodes Symbols Shape Attribute Based English_language

Layout Graph-Theoretic Layout Spring Embedding OK

Page 31: The Basics of Network Computing

A New View of the Network

Page 32: The Basics of Network Computing

Visualizing Contact Network Data

UCINet Spreadsheet File Open Excel Files Hybrid_Variable.xlsx

File Save As UCINet 7 dataset Hybrid_Variable

Visualize NetdrawFile Open Ucinet Dataset Network

Contact_Network_Data-ColAff File Open Ucinet Dataset Attribute data Hybrid_Variable

Page 33: The Basics of Network Computing
Page 34: The Basics of Network Computing

Visualizing Contact Network Data – Continued

Click on delete isolates buttonsLayout Graph Theoretic Layout Spring

Embedding (You may need to do this twice)Analysis Components OK

Page 35: The Basics of Network Computing
Page 36: The Basics of Network Computing

Visualizing Contact Network Data – Continued

Click on MC button to look at main component only

Turn off labels, arrow headsRepeat spring embeddingProperties Lines Size Tie Strength 1

to 10Properties Nodes Symbols Shape

Attribute Based Select Attribute Hybrid Variable OK

Click a node Choose label visible

Page 37: The Basics of Network Computing
Page 38: The Basics of Network Computing

Visualizing Contact Network Data – Continued

Analysis Subgroups Factions 2 (or 3 or 4) Go!

Page 39: The Basics of Network Computing
Page 40: The Basics of Network Computing

Next Steps

Multiplex VisualizationsThree Dimensional VisualizationsAdvanced analysis Exponential Random

Graph Models